`sklearn.feature_extraction.text`.CountVectorizer¶

class sklearn.feature_extraction.text.CountVectorizer(*, input='content', encoding='utf-8', decode_error='strict', strip_accents=None, lowercase=True, preprocessor=None, tokenizer=None, stop_words=None, token_pattern='(?u)\b\w\w+\b', ngram_range=(1, 1), analyzer='word', max_df=1.0, min_df=1, max_features=None, vocabulary=None, binary=False, dtype=<class 'numpy.int64'>)[source]¶

Convert a collection of text documents to a matrix of token counts

This implementation produces a sparse representation of the counts using scipy.sparse.csr_matrix.

If you do not provide an a-priori dictionary and you do not use an analyzer that does some kind of feature selection then the number of features will be equal to the vocabulary size found by analyzing the data.

See also

HashingVectorizer, TfidfVectorizer

Notes

The stop_words_ attribute can get large and increase the model size when pickling. This attribute is provided only for introspection and can be safely removed using delattr or set to None before pickling.

Examples

>>> from sklearn.feature_extraction.text import CountVectorizer
>>> corpus = [
...     'This is the first document.',
...     'This document is the second document.',
...     'And this is the third one.',
...     'Is this the first document?',
... ]
>>> vectorizer = CountVectorizer()
>>> X = vectorizer.fit_transform(corpus)
>>> print(vectorizer.get_feature_names())
['and', 'document', 'first', 'is', 'one', 'second', 'the', 'third', 'this']
>>> print(X.toarray())
[[0 1 1 1 0 0 1 0 1]
 [0 2 0 1 0 1 1 0 1]
 [1 0 0 1 1 0 1 1 1]
 [0 1 1 1 0 0 1 0 1]]
>>> vectorizer2 = CountVectorizer(analyzer='word', ngram_range=(2, 2))
>>> X2 = vectorizer2.fit_transform(corpus)
>>> print(vectorizer2.get_feature_names())
['and this', 'document is', 'first document', 'is the', 'is this',
'second document', 'the first', 'the second', 'the third', 'third one',
 'this document', 'this is', 'this the']
 >>> print(X2.toarray())
 [[0 0 1 1 0 0 1 0 0 0 0 1 0]
 [0 1 0 1 0 1 0 1 0 0 1 0 0]
 [1 0 0 1 0 0 0 0 1 1 0 1 0]
 [0 0 1 0 1 0 1 0 0 0 0 0 1]]

Methods

`build_analyzer`()	Return a callable that handles preprocessing, tokenization and n-grams generation.
`build_preprocessor`()	Return a function to preprocess the text before tokenization.
`build_tokenizer`()	Return a function that splits a string into a sequence of tokens.
`decode`(doc)	Decode the input into a string of unicode symbols.
`fit`(raw_documents[, y])	Learn a vocabulary dictionary of all tokens in the raw documents.
`fit_transform`(raw_documents[, y])	Learn the vocabulary dictionary and return document-term matrix.
`get_feature_names`()	Array mapping from feature integer indices to feature name.
`get_params`([deep])	Get parameters for this estimator.
`get_stop_words`()	Build or fetch the effective stop words list.
`inverse_transform`(X)	Return terms per document with nonzero entries in X.
`set_params`(**params)	Set the parameters of this estimator.
`transform`(raw_documents)	Transform documents to document-term matrix.

__init__(*, input='content', encoding='utf-8', decode_error='strict', strip_accents=None, lowercase=True, preprocessor=None, tokenizer=None, stop_words=None, token_pattern='(?u)\\b\\w\\w+\\b', ngram_range=(1, 1), analyzer='word', max_df=1.0, min_df=1, max_features=None, vocabulary=None, binary=False, dtype=<class 'numpy.int64'>)[source]¶: Initialize self. See help(type(self)) for accurate signature.

build_analyzer()[source]¶

Return a callable that handles preprocessing, tokenization and n-grams generation.

Returns

analyzer: callable: A function to handle preprocessing, tokenization and n-grams generation.

build_preprocessor()[source]¶

Return a function to preprocess the text before tokenization.

Returns

preprocessor: callable: A function to preprocess the text before tokenization.

build_tokenizer()[source]¶

Return a function that splits a string into a sequence of tokens.

Returns

tokenizer: callable: A function to split a string into a sequence of tokens.

decode(doc)[source]¶

Decode the input into a string of unicode symbols.

The decoding strategy depends on the vectorizer parameters.

Parameters

docstr: The string to decode.

Returns

doc: str: A string of unicode symbols.

fit(raw_documents, y=None)[source]¶

Learn a vocabulary dictionary of all tokens in the raw documents.

Parameters

raw_documentsiterable: An iterable which yields either str, unicode or file objects.

Returns

self

fit_transform(raw_documents, y=None)[source]¶

Learn the vocabulary dictionary and return document-term matrix.

This is equivalent to fit followed by transform, but more efficiently implemented.

Parameters

raw_documentsiterable: An iterable which yields either str, unicode or file objects.

Returns

Xarray of shape (n_samples, n_features): Document-term matrix.

get_feature_names()[source]¶

Array mapping from feature integer indices to feature name.

Returns

feature_nameslist: A list of feature names.

get_params(deep=True)[source]¶

Get parameters for this estimator.

Parameters

deepbool, default=True: If True, will return the parameters for this estimator and contained subobjects that are estimators.

Returns

paramsmapping of string to any: Parameter names mapped to their values.

get_stop_words()[source]¶

Build or fetch the effective stop words list.

Returns

stop_words: list or None: A list of stop words.

inverse_transform(X)[source]¶

Return terms per document with nonzero entries in X.

Parameters

X{array-like, sparse matrix} of shape (n_samples, n_features): Document-term matrix.

Returns

X_invlist of arrays of shape (n_samples,): List of arrays of terms.

set_params(**params)[source]¶

Set the parameters of this estimator.

The method works on simple estimators as well as on nested objects (such as pipelines). The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.

Parameters